Change
Detection
To
classify or not
to classify
There
are various ways
of approaching the
use of satellite
imagery for determining
change in urban
environments. We
can divide the methods
for change detection
into pre-classification
and post-classification
techniques. The
pre-classification
techniques apply
various algorithms directly to
multiple dates of
satellite imagery
to generate ‘‘change''
vs. ‘‘no-change''
maps. These techniques
locate where changes
took place but do
not provide information
on the nature of
the change. We do
not utilize pre-classification
techniques for the
material found on
this website.
Post-classification
comparison methods
use separate classifications
of images acquired
at different times
to produce difference
maps. ‘‘From–to''
change information
can be generated
telling us how
much change occurred
or what areas
changed from ___
to ___. The classification
of each date of
imagery builds
a historical series
that can be more
easily updated
and used for other
applications. The
post-classification
comparison approach
also compensates
for variation
in atmospheric
conditions and
vegetation between
dates since each
classification
is produced independently.
Map
Algebra
Whether
pre- or post classification
techniques are utilized,
the digital nature
of satellite imagery
and its layout in “grids” enables
us to literally
add, subtract, multiply,
divide or perform
any mathematical
formula on the aligned
pixels between images.
These operations
are commonly referred
to as “map
algebra.”
When
looking at change
between land cover
classes, the change
detection process
is straightforward.
A pixel or grid
cell will change
from one land cover
type to another
implying change,
or remain the same,
implying no change.
We can monitor
and analyze changes
between the classes,
such as agricultural
land transitioning
to urban or forested
land transitioning
to agriculture.
Seven classes produce
a possibility of
49 changes classes,
8 classes create
64 change classes,
and so on. The
set of change
maps displayed
for the TCMA
simply
monitor all rural
classes (agriculture,
forest, wetland)
that transitioned
to the urban class
during the time
period. For more
detailed analyses,
the set of TCMA
coverages can be
downloaded
and
manipulated
using GIS software.
Corresponding
statistics
can also be generated,
producing a census
of land cover
changes.
Land
Cover Class |
1986 |
1991 |
1998 |
2002 |
Relative
Change,
1986 – 2002
(%) |
Area
(000
ha) |
% |
Area
(000
ha) |
% |
Area
(000
ha) |
% |
Area
(000
ha) |
% |
Agriculture |
365 |
47.5 |
342 |
44.4 |
316 |
41.1 |
310 |
40.3 |
-15.1 |
Urban |
183 |
23.8 |
202 |
26.3 |
235 |
30.6 |
252 |
32.8 |
37.7 |
Forest |
112 |
14.6 |
111 |
14.4 |
106 |
13.8 |
103 |
13.4 |
-8.0 |
Wetland |
58 |
7.5 |
60 |
7.8 |
56 |
7.3 |
51 |
6.6 |
-12.1 |
Water |
42 |
5.5 |
46 |
6.0 |
46 |
5.9 |
43 |
5.6 |
2.4 |
Grass |
7.2 |
0.9 |
6.4 |
0.8 |
7.7 |
1.0 |
6.7 |
0.9 |
-6.9 |
Extraction |
1.9 |
0.2 |
2.4 |
0.3 |
2.7 |
0.4 |
2.6 |
0.3 |
36.8 |
Summary
of Landsat classification
area statistics
in the TCMA for
1986, 1991, 1998,
and 2002
Shades
of Difference
When
looking at change
in impervious surface
we are dealing
with a gradation
of classes, ranging
from zero to 100%.
In theory we have
10,000 possible
change classes,
which can quickly
become overwhelming.
Since our research
is mainly concerned
with the location
and intensity of
impervious surface
changes, it makes
sense to group
the change into
ranges of intensity.
Therefore, our impervious
surface change
maps reflect
areas where imperviousness
increased by various
sets of percentages.
This gives us a
sense for the intensity
of development
within the time
period viewed. For
example, if an area
increased in imperviousness
in the 76-100%
range we know that
the development
was quick and completely
covered. On the
other hand, an
area that has increased
by 10 - 25% can
highlight areas
where lower intensity
development or
incremental development
has taken place.
Change
Statistics
We
can also sum and
subset the total
change by geographical
boundaries such
as counties, cities,
watersheds and lakesheds.
See our more
detailed maps for this array
of information.
Change statistics
allow us to compare
growth patterns
between years and
areas. For example,
the table below
lists the amount
of impervious surface
area for several
selected cities
and the entire state.
Between 1990 and
2000 the amount
of impervious area
for the entire state
increased 145,830
hectares, from 1.2
to 1.9% of the total
land area, an increase
of 53%.
Impervious
area statistics
for selected cities
and the state
of Minnesota for
1990 and 2000
|
Total
Area (ha) |
1990
ISA (ha) |
2000
ISA (ha) |
Change
(ha) |
1990
% ISA |
2000
% ISA |
Pct.
Change |
St
Cloud |
10,405 |
2,066 |
2,894 |
829 |
19.9 |
27.8 |
40.1 |
Rochester |
11,932 |
2,405 |
2,953 |
548 |
20.2 |
24.8 |
22.8 |
Bemidji |
3,448 |
625 |
698 |
73 |
18.1 |
20.2 |
11.6 |
Brainerd |
2,936 |
518 |
575 |
57 |
17.6 |
19.6 |
10.9 |
Fergus
Falls |
3,879 |
405 |
634 |
229 |
10.4 |
16.3 |
56.7 |
Elk
River |
11,344 |
795 |
1,295 |
500 |
7.0 |
11.4 |
62.9 |
Sauk
Rapids |
1,409 |
323 |
499 |
176 |
22.9 |
35.4 |
54.7 |
Duluth |
22,601 |
3,055 |
3,044 |
-11 |
13.5 |
13.5 |
-0.4 |
Mankato |
4,290 |
939 |
1,414 |
474 |
21.9 |
32.9 |
50.5 |
Owatonna |
3,436 |
757 |
990 |
233 |
22.0 |
28.8 |
30.7 |
Eagan |
8,652 |
2,212 |
2,488 |
276 |
25.6 |
28.8 |
12.5 |
Plymouth |
9,142 |
1,709 |
2,170 |
461 |
18.7 |
23.7 |
27.0 |
Woodbury |
9,216 |
1,041 |
1,594 |
552 |
11.3 |
17.3 |
53.1 |
State |
21,851,634 |
272,863 |
418,693 |
145,830 |
1.2 |
1.9 |
53.4 |
|
